Semi-Supervised Learning Algorithm for Identifying High-Priority Drug-Drug Interactions Through Adverse Event Reports

Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDIrelated adverse events. However, the implementation of DDI alerting system remains a...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics Jg. 24; H. 1; S. 57 - 68
Hauptverfasser:	Liu, Ning, Chen, Cheng-Bang, Kumara, Soundar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adverse Drug Reaction Reporting Systems adverse event reports Algorithms classification clinical decision support Databases, Factual Decision Support Systems, Clinical drug-drug interactions Drug-Related Side Effects and Adverse Reactions - diagnosis Drug-Related Side Effects and Adverse Reactions - epidemiology Drug-Related Side Effects and Adverse Reactions - prevention & control Drugs Feature extraction Humans Information sources Learning algorithms Machine learning Machine learning algorithms Prediction algorithms Semi-supervised learning Semisupervised learning stacked autoencoder Supervised Machine Learning Support vector machines Workflow
ISSN:	2168-2194, 2168-2208, 2168-2208
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Abstract	Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDIrelated adverse events. However, the implementation of DDI alerting system remains a challenge as users are experiencing alert overload which causes alert fatigue. One strategy to optimize the current system is to establish a list of high-priority DDIs for alerting purposes, though it is a resource-intensive task. In this study, we propose a machine learning framework to extract useful features from the FDA adverse event reports and then identify potential highpriority DDIs using an autoencoder-based semi-supervised learning algorithm. The experimental results demonstrate the effectiveness of using adverse event feature representations in differentiating highand low-priority DDIs. Additionally, the proposed algorithm utilizes stacked autoencoders and weighted support vector machine for boosting classification performance, which outperforms other competing methods in terms of F-measure and AUC score. This framework integrates multiple information sources, leverages domain knowledge and clinical evidence, and provides a practical approach for pre-screening high-priority DDI candidates for medication alerts.
AbstractList	Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDI-related adverse events. However, the implementation of DDI alerting system remains a challenge as users are experiencing alert overload which causes alert fatigue. One strategy to optimize the current system is to establish a list of high-priority DDIs for alerting purposes, though it is a resource-intensive task. In this study, we propose a machine learning framework to extract useful features from the FDA adverse event reports and then identify potential high-priority DDIs using an autoencoder-based semi-supervised learning algorithm. The experimental results demonstrate the effectiveness of using adverse event feature representations in differentiating high- and low-priority DDIs. Additionally, the proposed algorithm utilizes stacked autoencoders and weighted support vector machine for boosting classification performance, which outperforms other competing methods in terms of F-measure and AUC score. This framework integrates multiple information sources, leverages domain knowledge and clinical evidence, and provides a practical approach for pre-screening high-priority DDI candidates for medication alerts. Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDIrelated adverse events. However, the implementation of DDI alerting system remains a challenge as users are experiencing alert overload which causes alert fatigue. One strategy to optimize the current system is to establish a list of high-priority DDIs for alerting purposes, though it is a resource-intensive task. In this study, we propose a machine learning framework to extract useful features from the FDA adverse event reports and then identify potential highpriority DDIs using an autoencoder-based semi-supervised learning algorithm. The experimental results demonstrate the effectiveness of using adverse event feature representations in differentiating highand low-priority DDIs. Additionally, the proposed algorithm utilizes stacked autoencoders and weighted support vector machine for boosting classification performance, which outperforms other competing methods in terms of F-measure and AUC score. This framework integrates multiple information sources, leverages domain knowledge and clinical evidence, and provides a practical approach for pre-screening high-priority DDI candidates for medication alerts. Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDI-related adverse events. However, the implementation of DDI alerting system remains a challenge as users are experiencing alert overload which causes alert fatigue. One strategy to optimize the current system is to establish a list of high-priority DDIs for alerting purposes, though it is a resource-intensive task. In this study, we propose a machine learning framework to extract useful features from the FDA adverse event reports and then identify potential high-priority DDIs using an autoencoder-based semi-supervised learning algorithm. The experimental results demonstrate the effectiveness of using adverse event feature representations in differentiating high- and low-priority DDIs. Additionally, the proposed algorithm utilizes stacked autoencoders and weighted support vector machine for boosting classification performance, which outperforms other competing methods in terms of F-measure and AUC score. This framework integrates multiple information sources, leverages domain knowledge and clinical evidence, and provides a practical approach for pre-screening high-priority DDI candidates for medication alerts.Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDI-related adverse events. However, the implementation of DDI alerting system remains a challenge as users are experiencing alert overload which causes alert fatigue. One strategy to optimize the current system is to establish a list of high-priority DDIs for alerting purposes, though it is a resource-intensive task. In this study, we propose a machine learning framework to extract useful features from the FDA adverse event reports and then identify potential high-priority DDIs using an autoencoder-based semi-supervised learning algorithm. The experimental results demonstrate the effectiveness of using adverse event feature representations in differentiating high- and low-priority DDIs. Additionally, the proposed algorithm utilizes stacked autoencoders and weighted support vector machine for boosting classification performance, which outperforms other competing methods in terms of F-measure and AUC score. This framework integrates multiple information sources, leverages domain knowledge and clinical evidence, and provides a practical approach for pre-screening high-priority DDI candidates for medication alerts.
Author	Chen, Cheng-Bang Kumara, Soundar Liu, Ning
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SubjectTerms	Adverse Drug Reaction Reporting Systems adverse event reports Algorithms classification clinical decision support Databases, Factual Decision Support Systems, Clinical drug-drug interactions Drug-Related Side Effects and Adverse Reactions - diagnosis Drug-Related Side Effects and Adverse Reactions - epidemiology Drug-Related Side Effects and Adverse Reactions - prevention & control Drugs Feature extraction Humans Information sources Learning algorithms Machine learning Machine learning algorithms Prediction algorithms Semi-supervised learning Semisupervised learning stacked autoencoder Supervised Machine Learning Support vector machines Workflow
Title	Semi-Supervised Learning Algorithm for Identifying High-Priority Drug-Drug Interactions Through Adverse Event Reports
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